As the paradigm of modern medicine shifts toward prevention and management, the importance of implantable electronics for real-time physiological monitoring and therapeutic intervention has surged, yet the mechanical mismatch between conventional rigid devices and soft tissues poses significant challenges regarding inflammation and long-term performance. Consequently, this review hierarchically analyzes advanced semiconductor integration strategies for flexible and stretchable implantable systems, utilizing Silicon Nanomembrane (SiNM) technology as a core building block to achieve mechanical compliance while maintaining CMOS compatibility. We systematically examine flexible substrate processing and patterning techniques, including laser-induced graphene (LIG) and printing methods, and place special emphasis on conformal encapsulation strategies using inorganic/organic multilayer thin films to ensure miniaturization and reliability in harsh biological environments. Furthermore, the review covers system-level integration issues, including hierarchical wireless communication strategies tailored to implantation depth and hybrid energy harvesting technologies for battery-free operation, ultimately proposing that the organic integration of these elements is essential for realizing next-generation "Fully Autonomous Bio-integrated Systems".

Microgravity profoundly influences cellular morphology and function, yet ground-based simulation requires systems capable of randomizing the gravity vector. A dual-axis rotating machine (RM), such as a clinostat and random positioning machine (RPM), achieves quasi-isotropic gravitational averaging, but its quantitative optimization remains unclear. Here, a computational-experimental framework was established to evaluate dynamic isotropy of a dual-axis RM. Gravity vector trajectories were simulated across rotation ratios, and five metrics-Spherical Coverage (C), Cumulative Bias (H), Normalized Shannon Entropy (H/Hₘₐₓ), Mean Resultant Length (R), and Degree of Gravity Dispersion (DGD)-were integrated into an isotropy index (Sₒₚₜ). The isotropy peaked within an asynchronous range (4.0:3.8, 4.0:3.6, and 4.0:3.4rpm), where gravity vectors exhibited uniform spherical coverage and minimal bias. To further assess the influence of rotational strength beyond the rotational ratio, an additional experiment was conducted using Huh7 hepatocellular carcinoma spheroids. Under high-speed random rotation (~ 4.0rpm), cells formed compact, circular spheroids, whereas low-speed (~ 1.0rpm) or static cultures produced irregular aggregates. These findings indicate that not only the ratio between dual-axis rotations but also the absolute rotational intensity critically affects isotropy and cellular morphogenesis under simulated microgravity. This integrative framework establishes a quantitative foundation for optimizing dual-axis rotating machines.

The online version contains supplementary material available at 10.1007/s13534-025-00533-0.

Brain-computer interfaces (BCIs) based on motor imagery (MI) electroencephalogram (EEG) signals have shown tremendous potential in neurorehabilitation due to their non-invasive acquisition and ease of use. However, the cross-session nature of EEG signals-where recordings from the same subject at different sessions may vary due to fluctuations in physiological state and environmental conditions-presents a significant challenge. Efficient extraction and preservation of temporal and spatial features from EEG signals can capture invariant neural activation patterns while suppressing session-dependent noise and variability, thereby greatly enhancing the robustness of cross‑session motor imagery classification. To address the suboptimal performance of existing models in cross-session MI-EEG classification, this paper proposes Spatial-Shift Attention Deformable Convolution Network-SSA-DCNet, a compact convolutional neural network in which temporal filtering is implemented via a two-dimensional deformable convolution of size 1 × 64, so that the sampling grid dynamically adapts to the non-uniform distributions of informative EEG segments while operating on a 1 × 64 kernel along the temporal axis. Thereafter, a spatial-shift attention architecture expands each intermediate feature map from C to 3C channels, evenly splits them into three subsets, applies distinct spatial-shift operations to each subset, and finally merges them via a split-attention that recalibrates channel weights to emphasize spatial patterns stable across sessions. On the public BCI Competition IV-2a and 2b datasets, SSA-DCNet achieved classification accuracies of 84.72% and 90.45%, respectively. Moreover, t-SNE visualizations provide intuitive evidence, underscoring its superior discriminative power and robust cross-session generalization.

The global aging population has raised concerns regarding age-related health issues like osteoporosis and bone fractures. To address these conditions, bone-like scaffolds containing bioactive molecules and biomaterials have been widely studied. However, uncontrolled burst release and delivery of drugs can incur negative side effects. To overcome this issue, a collagen-hydroxyapatite scaffold (COHAS) that can sequentially deliver Bone morphogenetic protein-2 (BMP-2) and Osteoprotegerin fused to the Fc region of immunoglobulin (OPG-Fc) is synthesized. The COHAS comprises a collagen-hydroxyapatite matrix containing BMP-2 and numerous poly-lactic glycolic acid (PLGA) microspheres with OPG-Fc, dispersed in the matrix. The dispersion of PLGA microspheres enables the retardation of OPG-Fc release compared to BMP-2 release. The controlled sequential delivery of BMP-2 and OPG-Fc exhibits synergistic potential in promoting new bone formation by simultaneously activating osteoblasts and deactivating osteoclasts. This investigation revealed that the COHAS co-loaded with BMP-2 and OPG-Fc possesses excellent cell viability and enhanced osteogenic properties in vitro. In vivo assessment via implantation of the drug-loaded COHAS using an 8mm-calvarial defect rat model demonstrated high efficacy of new bone formation with good biocompatibility. Hence, these findings provide valuable insights for developing therapeutic scaffolds capable of sequential release of multiple drugs, with the potential to extend a cell-free treatment system for bone regeneration.

This study proposes a visualization and analysis method for eye blinking pattern using high-frame-rate videos. The high-frame-rate video clips for visualization are taken using a camera without additional equipment. The partial video clips of eye blinking except for eyelid flutters and microsleeps are extracted from the entire video clip. The changes in shapes and positions of the upper eyelid during the eye blinking sequences are evaluated, and each eye blinking is visualized as a single image. The various parameters regarding eye blinking are calculated to analyze blinking patterns. The single eye blinking sequence is divided into phases to analyze and classify eye blinking patterns in more detail. In this experiment conducted on 80 volunteers, the proposed method was able to quantitatively analyze eyelid movements, and various parameters related to eye blinking were calculated. Additionally, different types of eye blinking patterns were visualized as graph images, and incomplete eye blinking and consecutive eye blinking were defined and detected. The proposed method can overcome the spatial and situational limitations of conventional bio-signal analysis methods, as it allows non-contact measurement in ordinary environments. In addition, since quantitative eye blink data obtained from high-frame-rate video contain more information than data obtained from bio-signals, it is expected that analysis methods using videos can be easily applied to a wider range of fields.

There has been growing interest in sleep tracking technologies utilizing ring-shaped wearable devices. This study aimed to develop a method for accurately estimating sleep duration, onset, and offset using such a device. Conventional wrist-worn accelerometer-based devices often show limited accuracy, particularly during periods of low movement. Likewise, algorithms relying solely on electrodermal activity (EDA) signals struggle to detect frequent wake episodes due to their low temporal resolution. To address these limitations, we developed a ring-shaped wearable device and a set of algorithms that integrate both accelerometer and EDA signals. The performance of the proposed algorithms was evaluated through a clinical study involving 25 participants, the majority of whom had sleep disorders. Results showed that combining these complementary signals enabled accurate detection of sleep onset and offset within 10min and maintained high accuracy even in conditions such as minimal movement or frequent wake episodes. These findings suggest that multimodal sensing may offer a promising direction for enhancing the reliability of sleep monitoring in real-world settings.

The online version contains supplementary material available at 10.1007/s13534-025-00525-0.

Cardiovascular disease stands as the leading cause of death globally, and substantial research has revealed its close correlation with the distribution of epicardial adipose tissue (EAT). Moreover, existing studies have demonstrated that EAT exhibits significant differences in distribution patterns and pathophysiological roles across various anatomical regions of the heart. Therefore, the quantitative analysis of EAT at different cardiac locations is crucial, and fine-grained segmentation of EAT via cardiac CT is an efficient method for obtaining the corresponding metrics. The existing computer-aided segmentation approaches typically treat EAT as a unified whole, which fails to meet the demands of nuanced diagnostics, and faces challenges such as class imbalance, thin structures, and anatomical variation, leading to low segmentation accuracy, limiting its application in cardiovascular disease risk assessment. To address these issues, we extend the existing segmentation strategy to the fine-grained segmentation of the left ventricle- (LV-), right ventricle- (RV-), and peri-atrium- (PA-) EAT, and propose the PRAEE framework based on position priors and edge enhancement. The core innovations of the proposed method are as follows: (1)Position-Prior Regularization: Considering the spatial distribution patterns of EAT in different anatomical regions, we design a regularization module that incorporates prior knowledge of typical spatial locations of various types of EAT as auxiliary constraints. This mechanism effectively guides the model to more accurately localize and differentiate EAT across anatomical regions, enabling an initial segmentation.(2)Adaptive Edge Enhancement: To further improve segmentation accuracy, we develop an edge enhancement module that explicitly extracts critical edge information through morphological operations and integrates it into the network architecture, significantly refining segmentation along boundary regions. Our approach has been validated on both a self-collected EAT dataset and the publicly available ACDC and MM-WHS datasets, demonstrating its effectiveness in enhancing fine-grained discrimination and edge detail performance.

Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.